In [53]:
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
# import seaborn as sns 
import datetime
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import Scatter, Figure, Layout
import plotly
import plotly.graph_objs as go
import plotly.express as px
init_notebook_mode(connected=False)
import io
import requests
import re

COVID-19 in Italy. Visuals


 


Data source: this GitHubi page

Authors and sources mentioned: Editore/Autore del dataset: Dipartimento della Protezione Civile. Categoria ISO 19115: Salute. Dati forniti dal Ministero della Salute.

Regional data files (Dati per Regione):
  • Struttura file giornaliero: dpc-covid19-ita-regioni-yyyymmdd.csv (dpc-covid19-ita-regioni-20200224.csv)
  • File complessivo: dpc-covid19-ita-regioni.csv
  • File ultimi dati (latest): dpc-covid19-ita-regioni-latest.csv

 


In [63]:
URL='https://it.wikipedia.org/wiki/Regione_(Italia)'
res=requests.get(URL)
tables=pd.read_html(res.text)
dt = tables[13]
In [64]:
dt2 = dt[['Regione','Popolazione (ab.)']].copy()
dt2.columns = ['Region','Pop']

def dewhite(x):
    ''.join(re.findall('\d+', x))
    
dt2.Pop = dt2.Pop.apply(lambda x: ''.join(re.findall('\d+', x))).astype(int)
Current data as of the date:
In [65]:
s = requests.get("https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv").content
dat = pd.read_csv(io.StringIO(s.decode('utf-8')))
print(dat.data.max())
2020-10-12T17:00:00

 

What's in the original dataframe?

In [66]:
print('Showing all variable names')
dat.columns
Showing all variable names in the original dataframe
Out[66]:
Index(['data', 'stato', 'codice_regione', 'denominazione_regione', 'lat',
       'long', 'ricoverati_con_sintomi', 'terapia_intensiva',
       'totale_ospedalizzati', 'isolamento_domiciliare', 'totale_positivi',
       'variazione_totale_positivi', 'nuovi_positivi', 'dimessi_guariti',
       'deceduti', 'casi_da_sospetto_diagnostico', 'casi_da_screening',
       'totale_casi', 'tamponi', 'casi_testati', 'note'],
      dtype='object')

 

Rows for the last 5 days

In [81]:
df = dat.drop(['stato','codice_regione'], axis=1)
df.columns = ['Date','Region','Lat','Long','HospWithSymptoms','IC','HospTotal','AtHome','CurrentlyPositive','VariationOfPositives','NewPositives','Recovered', 'Deaths','Diagnostico','Screening','TotalCases','NoOfTests','casi_testati','note']

df = pd.merge(df, dt2, left_on='Region', right_on='Region')

df['Date'] = pd.to_datetime(df['Date']).dt.date
df = df.set_index(df["Date"])
df.index = pd.to_datetime(df.index)

df['NewPositives'] = np.abs(df['NewPositives'])

dat.tail(5)
Out[81]:
data stato codice_regione denominazione_regione lat long ricoverati_con_sintomi terapia_intensiva totale_ospedalizzati isolamento_domiciliare ... variazione_totale_positivi nuovi_positivi dimessi_guariti deceduti casi_da_sospetto_diagnostico casi_da_screening totale_casi tamponi casi_testati note
4867 2020-10-12T17:00:00 ITA 19 Sicilia 38.115697 13.362357 404 42 446 4236 ... 281 298 4571 339 6133.0 3459.0 9592 552826 397546.0 NaN
4868 2020-10-12T17:00:00 ITA 9 Toscana 43.769231 11.255889 205 40 245 6259 ... 362 466 10942 1180 14189.0 4437.0 18626 848466 570706.0 NaN
4869 2020-10-12T17:00:00 ITA 10 Umbria 43.106758 12.388247 61 9 70 1300 ... 134 148 2012 90 1844.0 1628.0 3472 233252 138736.0 NaN
4870 2020-10-12T17:00:00 ITA 2 Valle d'Aosta 45.737503 7.320149 9 3 12 213 ... 28 32 1125 146 1352.0 144.0 1496 31492 21680.0 NaN
4871 2020-10-12T17:00:00 ITA 5 Veneto 45.434905 12.338452 244 29 273 6016 ... 201 328 23323 2219 22250.0 9581.0 31831 2069955 808554.0 NaN

5 rows × 21 columns


 

Variables names into English and their explanation

  • Date : Date
  • HospWithSymptoms : Currently hospitalized patients with symptoms
  • IC : Intensive care
  • HospTotal: Total number of currently hospitalized patients
  • AtHome : Currently at home confinement
  • CurrentlyPositive : Total amount of current positive cases (Hospitalised patients + Home confinement)
  • NewPositives : New amount of positive cases (Actual total amount of current positive cases - total amount of current positive cases of the previous day)
  • Recovered : Recovered
  • Deaths : Deaths
  • TotalCases : Total amount of positive cases
  • NoOfTests : Tests performed

 

(double click and click on legend to select one or multiple regions in the graph)

In [69]:
fig = px.line(df2, x=df2.index, y="NewPositives", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Daily new cases, absolute numbers")
fig.show()
In [70]:
df2['MovAv7'] = df2['NewPositives'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x=df2.index[df2.index>'2020-3-1'], y="MovAv7", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="1-week rolling average of daily new cases")
fig.show()
In [71]:
df2['NewPos_pc'] = df2['NewPositives']/df2['Pop']*1000_000

df2['NewPos_pc'] = df2['NewPos_pc'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x=df2.index[df2.index>'2020-3-1'], y="NewPos_pc", color="Region", 
              hover_name="Region", log_y=False)
fig.update_layout(title="1-week rolling average of daily new cases, per million")
fig.show()
In [72]:
df2['IC_pc'] = df2['IC']/df2['Pop']*1000_000

fig = px.line(df2, x="Date", y="IC_pc", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current intensive care patients, per million")
fig.show()
In [73]:
df2['Hosp_pc'] = df2['HospTotal']/df2['Pop']*1000000

fig = px.line(df2, x="Date", y="Hosp_pc", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current hospitalized, per million")
fig.show()
In [75]:
df3 = df2.copy()

df3['NewDeaths'] = df3['Deaths'] - df3.groupby(['Region'])['Deaths'].transform('shift')

fig = px.bar(df3, x=df3['Date'], y="NewDeaths", color="Region", hover_name="Date")
fig.update_layout(title="Daily number of new deaths, absolute numbers")
fig.show()
In [76]:
df2['NewNoOfTests'] = df2['NoOfTests'] - df2.groupby(['Region'])['NoOfTests'].transform('shift')
df2.head()

df2['New_per_test'] = df2['NewPositives']/df2['NewNoOfTests']*100

fig = px.line(df2[df2['Region'].isin(['Lombardia','Veneto','Emilia-Romagna','Piemonte','Liguria'])], 
              x=df2[df2['Region'].isin(['Lombardia','Veneto','Emilia-Romagna','Piemonte','Liguria'])].index, y="New_per_test", color="Region", hover_name="Region",
        render_mode="svg", log_y=True, line_shape='spline')
fig.update_layout(title="New positive cases in daily tests in Northern regions, %")
fig.show()
In [77]:
df2['Deaths_per_mio'] = (df2['Deaths']/df2['Pop'])*1000_000
fig = px.line(df2, x="Date", y="Deaths_per_mio", color="Region", 
              hover_name="Region", render_mode="svg", line_shape='spline')
fig.update_layout(title="Cumulative number of deaths, per million")
fig.show()
In [78]:
df2['Change_per_mio'] = df2['VariationOfPositives']/df2['Pop']*1000_000
df2['Change_per_mio'] = df2['Change_per_mio'].rolling(window=7).mean()


# [df2['Region'].isin(['Lombardia','Veneto','Emilia-Romagna','Piemonte','Liguria'])]
fig = px.line(df2[(df2.index>'2020-3-1') & (df2['Region']!="""Valle d'Aosta""")], x='Date', y="Change_per_mio", color="Region", hover_name="Date")
fig.update_layout(title="1-week rolling average of daily change in positive cases, per million (excl. Valle d'Aosta)")
fig.show()

 

Italy as a whole

Data from all regions aggregated

In [80]:
df2 = df
df_sum = df2.drop(['Lat','Long'], axis=1).groupby(df.Date).sum().reset_index()

df_sum2 = pd.melt(df_sum, id_vars=['Date'], value_vars=['NewPositives','IC','HospTotal'])

fig = px.line(df_sum2, x="Date", y="value", color='variable', hover_name="value", render_mode="svg", log_y=True, 
              line_shape='spline')
fig.update_layout(title="Number of daily new positive cases, current IC patients and total hospitalized")
fig.show()
In [ ]: